!pip install -U torch torchvision
!pip install git+https://github.com/facebookresearch/fvcore.git
!git clone https://github.com/facebookresearch/detectron2 detectron2_repo
!pip install -e detectron2_repo
Collecting torch
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  Downloading https://files.pythonhosted.org/packages/fc/23/d418c9102d4054d19d57ccf0aca18b7c1c1f34cc0a136760b493f78ddb06/torchvision-0.4.1-cp36-cp36m-manylinux1_x86_64.whl (10.1MB)
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Installing collected packages: torch, torchvision
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Collecting git+https://github.com/facebookresearch/fvcore.git
  Cloning https://github.com/facebookresearch/fvcore.git to /tmp/pip-req-build-w1yn64cw
  Running command git clone -q https://github.com/facebookresearch/fvcore.git /tmp/pip-req-build-w1yn64cw
Collecting yacs>=0.1.6 (from fvcore==0.1)
  Downloading https://files.pythonhosted.org/packages/2f/51/9d613d67a8561a0cdf696c3909870f157ed85617fea3cff769bb7de09ef7/yacs-0.1.6-py3-none-any.whl
Collecting pyyaml>=5.1 (from fvcore==0.1)
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  Downloading https://files.pythonhosted.org/packages/60/ec/836a494dbaa72541f691ec4e66f29fdc8db9bcc7f49e1c2d457ba13ced42/portalocker-1.5.1-py2.py3-none-any.whl
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Building wheels for collected packages: fvcore, pyyaml
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  Stored in directory: /tmp/pip-ephem-wheel-cache-lgmu2lb2/wheels/48/53/79/3c6485543a4455a0006f5db590ab9957622b6227011941de06
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  Stored in directory: /root/.cache/pip/wheels/d9/45/dd/65f0b38450c47cf7e5312883deb97d065e030c5cca0a365030
Successfully built fvcore pyyaml
Installing collected packages: pyyaml, yacs, portalocker, fvcore
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Cloning into 'detectron2_repo'...
remote: Enumerating objects: 458, done.
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Obtaining file:///content/detectron2_repo
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Collecting tqdm>4.29.0 (from detectron2==0.1)
  Downloading https://files.pythonhosted.org/packages/e1/c1/bc1dba38b48f4ae3c4428aea669c5e27bd5a7642a74c8348451e0bd8ff86/tqdm-4.36.1-py2.py3-none-any.whl (52kB)
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Installing collected packages: tqdm, detectron2
  Found existing installation: tqdm 4.28.1
    Uninstalling tqdm-4.28.1:
      Successfully uninstalled tqdm-4.28.1
  Running setup.py develop for detectron2
Successfully installed detectron2 tqdm-4.36.1
import detectron2, cv2, random
import os, json, itertools
import numpy as np
import torch, torchvision
from detectron2.utils.logger import setup_logger
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.structures import BoxMode
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
from matplotlib import pyplot as plt
from google.colab.patches import cv2_imshow

setup_logger()
<Logger detectron2 (DEBUG)>

Inference With Pre-Trained Model

!wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
im = cv2.imread("./input.jpg")
cv2_imshow(im)
--2019-10-11 09:46:46--  http://images.cocodataset.org/val2017/000000439715.jpg
Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.106.116
Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.106.116|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 209222 (204K) [image/jpeg]
Saving to: ‘input.jpg’

input.jpg           100%[===================>] 204.32K   847KB/s    in 0.2s    

2019-10-11 09:46:47 (847 KB/s) - ‘input.jpg’ saved [209222/209222]

cfg = get_cfg()

# get model from https://github.com/facebookresearch/detectron2/tree/master/configs
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
# Weights ke pkl kre -_-
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"

predictor = DefaultPredictor(cfg)
outputs = predictor(im)
WARNING [10/11 09:46:54 d2.config.compat]: Config './detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
# look at the outputs
print('Classes of instances:', outputs["instances"].pred_classes)
print('Bboxes of instances:', outputs["instances"].pred_boxes)
Classes of instances: tensor([17,  0,  0,  0,  0,  0,  0,  0, 25,  0, 25, 25,  0,  0, 24],
       device='cuda:0')
Bboxes of instances: Boxes(tensor([[126.6035, 244.8977, 459.8291, 480.0000],
        [251.1083, 157.8127, 338.9731, 413.6379],
        [114.8496, 268.6864, 148.2352, 398.8111],
        [  0.8217, 281.0327,  78.6073, 478.4210],
        [ 49.3954, 274.1229,  80.1545, 342.9808],
        [561.2248, 271.5816, 596.2755, 385.2552],
        [385.9072, 270.3125, 413.7130, 304.0397],
        [515.9295, 278.3744, 562.2792, 389.3802],
        [335.2409, 251.9167, 414.7491, 275.9375],
        [350.9300, 269.2060, 386.0984, 297.9081],
        [331.6292, 230.9996, 393.2759, 257.2009],
        [510.7349, 263.2656, 570.9865, 295.9194],
        [409.0841, 271.8646, 460.5582, 356.8722],
        [506.8766, 283.3257, 529.9404, 324.0392],
        [594.5663, 283.4820, 609.0577, 311.4124]], device='cuda:0'))
# They also have visualization utils :P
v = Visualizer(im, MetadataCatalog.get("coco_2017_val"), scale = 1.5)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(v.get_image()[:, :, ::-1])

Training on Custom Dataset

!wget https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip
!unzip balloon_dataset.zip > /dev/null
--2019-10-11 09:47:11--  https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip
Resolving github.com (github.com)... 140.82.118.3
Connecting to github.com (github.com)|140.82.118.3|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20191011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191011T094712Z&X-Amz-Expires=300&X-Amz-Signature=7d6e77a1f62604e8eeea14871837711e05770f7ccefe7876722acc45d79590c6&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream [following]
--2019-10-11 09:47:13--  https://github-production-release-asset-2e65be.s3.amazonaws.com/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20191011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191011T094712Z&X-Amz-Expires=300&X-Amz-Signature=7d6e77a1f62604e8eeea14871837711e05770f7ccefe7876722acc45d79590c6&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream
Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.165.179
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HTTP request sent, awaiting response... 200 OK
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Saving to: ‘balloon_dataset.zip.1’

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replace balloon/train/via_region_data.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: n
replace __MACOSX/balloon/train/._via_region_data.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: n
replace balloon/train/53500107_d24b11b3c2_b.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: n
replace __MACOSX/balloon/train/._53500107_d24b11b3c2_b.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: 
def get_balloon_dicts(img_dir):
    json_file = os.path.join(img_dir, "via_region_data.json")
    with open(json_file) as f:
        imgs_anns = json.load(f)

    dataset_dicts = []
    for _, v in imgs_anns.items():
        record = {}
        
        filename = os.path.join(img_dir, v["filename"])
        height, width = cv2.imread(filename).shape[:2]
        
        record["file_name"] = filename
        record["height"] = height
        record["width"] = width
      
        annos = v["regions"]
        objs = []
        for _, anno in annos.items():
            assert not anno["region_attributes"]
            anno = anno["shape_attributes"]
            px = anno["all_points_x"]
            py = anno["all_points_y"]
            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
            poly = list(itertools.chain.from_iterable(poly))

            obj = {
                "bbox": [np.min(px), np.min(py), np.max(px), np.max(py)],
                "bbox_mode": BoxMode.XYXY_ABS,
                "segmentation": [poly],
                "category_id": 0,
                "iscrowd": 0
            }
            objs.append(obj)
        record["annotations"] = objs
        dataset_dicts.append(record)
    return dataset_dicts
get_balloon_dicts("./balloon/train")
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    'bbox_mode': <BoxMode.XYXY_ABS: 0>,
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   {'bbox': [75, 400, 131, 495],
    'bbox_mode': <BoxMode.XYXY_ABS: 0>,
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    'segmentation': [[96.5,
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  'file_name': './balloon/train/120853323_d4788431b9_b.jpg',
  'height': 679,
  'width': 1024},
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# Register Balloon Dataset
for d in ["train", "val"]:
    DatasetCatalog.register("balloon/" + d, lambda d=d: get_balloon_dicts("balloon/" + d))
    MetadataCatalog.get("balloon/" + d).set(thing_classes=["balloon"])
balloon_metadata = MetadataCatalog.get("balloon/train")
# Visualize Dataset
dataset_dicts = get_balloon_dicts("balloon/train")
for d in random.sample(dataset_dicts, 3):
    img = cv2.imread(d["file_name"])
    visualizer = Visualizer(img[:, :, ::-1], metadata=balloon_metadata, scale=0.5)
    vis = visualizer.draw_dataset_dict(d)
    cv2_imshow(vis.get_image()[:, :, ::-1])
# Set the training Configs
cfg = get_cfg()
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.DATASETS.TRAIN = ("balloon/train",)
cfg.DATASETS.TEST = ()   # no metrics implemented for this dataset
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"  # initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 300    # 300 iterations seems good enough, but you can certainly train longer
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # faster, and good enough for this toy dataset
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1  # only has one class (ballon)
WARNING [10/11 09:48:44 d2.config.compat]: Config './detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
# Trainer
os.makedirs(cfg.OUTPUT_DIR, exist_ok = True)
trainer = DefaultTrainer(cfg) 
trainer.resume_or_load(resume = False)
trainer.train()
[10/11 09:48:55 d2.engine.defaults]: Model:
GeneralizedRCNN(
  (backbone): FPN(
    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (top_block): LastLevelMaxPool()
    (bottom_up): ResNet(
      (stem): BasicStem(
        (conv1): Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
        )
      )
      (res2): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv1): Conv2d(
            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
      )
      (res3): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv1): Conv2d(
            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
      )
      (res4): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
          (conv1): Conv2d(
            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (4): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (5): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
      )
      (res5): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
          (conv1): Conv2d(
            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
      )
    )
  )
  (proposal_generator): RPN(
    (anchor_generator): DefaultAnchorGenerator(
      (cell_anchors): BufferList()
    )
    (rpn_head): StandardRPNHead(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  (roi_heads): StandardROIHeads(
    (box_pooler): ROIPooler(
      (level_poolers): ModuleList(
        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
      )
    )
    (box_head): FastRCNNConvFCHead(
      (fc1): Linear(in_features=12544, out_features=1024, bias=True)
      (fc2): Linear(in_features=1024, out_features=1024, bias=True)
    )
    (box_predictor): FastRCNNOutputLayers(
      (cls_score): Linear(in_features=1024, out_features=2, bias=True)
      (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)
    )
    (mask_pooler): ROIPooler(
      (level_poolers): ModuleList(
        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
        (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
        (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
        (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
      )
    )
    (mask_head): MaskRCNNConvUpsampleHead(
      (mask_fcn1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (mask_fcn2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (mask_fcn3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (mask_fcn4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
      (predictor): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))
    )
  )
)
[10/11 09:48:57 d2.data.build]: Removed 0 images with no usable annotations. 61 images left.
[10/11 09:48:57 d2.data.build]: Distribution of training instances among all 1 categories:
|  category  | #instances   |
|:----------:|:-------------|
|  balloon   | 255          |
|            |              |
[10/11 09:48:57 d2.data.detection_utils]: TransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[10/11 09:48:57 d2.data.build]: Using training sampler TrainingSampler
'roi_heads.box_predictor.cls_score.weight' has shape (81, 1024) in the checkpoint but (2, 1024) in the model! Skipped.
'roi_heads.box_predictor.cls_score.bias' has shape (81,) in the checkpoint but (2,) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.weight' has shape (320, 1024) in the checkpoint but (4, 1024) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.bias' has shape (320,) in the checkpoint but (4,) in the model! Skipped.
'roi_heads.mask_head.predictor.weight' has shape (80, 256, 1, 1) in the checkpoint but (1, 256, 1, 1) in the model! Skipped.
'roi_heads.mask_head.predictor.bias' has shape (80,) in the checkpoint but (1,) in the model! Skipped.
[10/11 09:49:01 d2.engine.train_loop]: Starting training from iteration 0
[10/11 09:49:25 d2.utils.events]: eta: 0:05:35  iter: 19  total_loss: 2.006  loss_cls: 0.685  loss_box_reg: 0.608  loss_mask: 0.692  loss_rpn_cls: 0.021  loss_rpn_loc: 0.005  time: 1.2004  data_time: 0.0035  lr: 0.000005  max_mem: 2725M
[10/11 09:49:49 d2.utils.events]: eta: 0:05:07  iter: 39  total_loss: 2.141  loss_cls: 0.650  loss_box_reg: 0.658  loss_mask: 0.663  loss_rpn_cls: 0.040  loss_rpn_loc: 0.010  time: 1.2014  data_time: 0.0038  lr: 0.000010  max_mem: 2726M
[10/11 09:50:14 d2.utils.events]: eta: 0:04:53  iter: 59  total_loss: 1.734  loss_cls: 0.581  loss_box_reg: 0.583  loss_mask: 0.603  loss_rpn_cls: 0.018  loss_rpn_loc: 0.005  time: 1.2228  data_time: 0.0035  lr: 0.000015  max_mem: 2850M
[10/11 09:50:38 d2.utils.events]: eta: 0:04:26  iter: 79  total_loss: 1.721  loss_cls: 0.509  loss_box_reg: 0.633  loss_mask: 0.538  loss_rpn_cls: 0.033  loss_rpn_loc: 0.007  time: 1.2148  data_time: 0.0039  lr: 0.000020  max_mem: 2850M
[10/11 09:51:03 d2.utils.events]: eta: 0:04:04  iter: 99  total_loss: 1.681  loss_cls: 0.442  loss_box_reg: 0.700  loss_mask: 0.479  loss_rpn_cls: 0.031  loss_rpn_loc: 0.008  time: 1.2220  data_time: 0.0036  lr: 0.000025  max_mem: 2850M
[10/11 09:51:29 d2.utils.events]: eta: 0:03:42  iter: 119  total_loss: 1.519  loss_cls: 0.408  loss_box_reg: 0.611  loss_mask: 0.401  loss_rpn_cls: 0.028  loss_rpn_loc: 0.004  time: 1.2303  data_time: 0.0036  lr: 0.000030  max_mem: 2850M
[10/11 09:51:54 d2.utils.events]: eta: 0:03:18  iter: 139  total_loss: 1.490  loss_cls: 0.370  loss_box_reg: 0.696  loss_mask: 0.364  loss_rpn_cls: 0.018  loss_rpn_loc: 0.011  time: 1.2313  data_time: 0.0036  lr: 0.000035  max_mem: 2850M
[10/11 09:52:18 d2.utils.events]: eta: 0:02:53  iter: 159  total_loss: 1.293  loss_cls: 0.307  loss_box_reg: 0.611  loss_mask: 0.303  loss_rpn_cls: 0.021  loss_rpn_loc: 0.006  time: 1.2321  data_time: 0.0034  lr: 0.000040  max_mem: 2850M
[10/11 09:52:43 d2.utils.events]: eta: 0:02:30  iter: 179  total_loss: 1.221  loss_cls: 0.279  loss_box_reg: 0.629  loss_mask: 0.287  loss_rpn_cls: 0.024  loss_rpn_loc: 0.011  time: 1.2337  data_time: 0.0035  lr: 0.000045  max_mem: 2850M
[10/11 09:53:08 d2.utils.events]: eta: 0:02:05  iter: 199  total_loss: 1.203  loss_cls: 0.242  loss_box_reg: 0.639  loss_mask: 0.241  loss_rpn_cls: 0.020  loss_rpn_loc: 0.006  time: 1.2365  data_time: 0.0035  lr: 0.000050  max_mem: 2850M
[10/11 09:53:34 d2.utils.events]: eta: 0:01:40  iter: 219  total_loss: 1.109  loss_cls: 0.234  loss_box_reg: 0.659  loss_mask: 0.205  loss_rpn_cls: 0.015  loss_rpn_loc: 0.011  time: 1.2379  data_time: 0.0035  lr: 0.000055  max_mem: 2850M
[10/11 09:53:59 d2.utils.events]: eta: 0:01:16  iter: 239  total_loss: 1.023  loss_cls: 0.202  loss_box_reg: 0.596  loss_mask: 0.186  loss_rpn_cls: 0.021  loss_rpn_loc: 0.009  time: 1.2428  data_time: 0.0034  lr: 0.000060  max_mem: 2850M
[10/11 09:54:24 d2.utils.events]: eta: 0:00:51  iter: 259  total_loss: 0.989  loss_cls: 0.192  loss_box_reg: 0.599  loss_mask: 0.179  loss_rpn_cls: 0.020  loss_rpn_loc: 0.007  time: 1.2424  data_time: 0.0035  lr: 0.000065  max_mem: 2850M
[10/11 09:54:49 d2.utils.events]: eta: 0:00:26  iter: 279  total_loss: 0.944  loss_cls: 0.155  loss_box_reg: 0.564  loss_mask: 0.151  loss_rpn_cls: 0.026  loss_rpn_loc: 0.011  time: 1.2431  data_time: 0.0034  lr: 0.000070  max_mem: 2850M
[10/11 09:55:17 d2.utils.events]: eta: 0:00:01  iter: 299  total_loss: 0.761  loss_cls: 0.135  loss_box_reg: 0.468  loss_mask: 0.119  loss_rpn_cls: 0.017  loss_rpn_loc: 0.006  time: 1.2489  data_time: 0.0035  lr: 0.000075  max_mem: 2850M
[10/11 09:55:18 d2.engine.hooks]: Overall training speed: 297 iterations in 0:06:12 (1.2531 s / it)
[10/11 09:55:18 d2.engine.hooks]: Total training time: 0:06:14 (0:00:02 on hooks)
OrderedDict()
# Save model for testing
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7   # set the testing threshold for this model
cfg.DATASETS.TEST = ("balloon_val", )
predictor = DefaultPredictor(cfg)
# Inference on Validation Set

from detectron2.utils.visualizer import ColorMode
dataset_dicts = get_balloon_dicts("balloon/val")
for d in random.sample(dataset_dicts, 3):    
    im = cv2.imread(d["file_name"])
    outputs = predictor(im)
    v = Visualizer(im[:, :, ::-1],
                   metadata=MetadataCatalog.get("balloon_val"), 
                   scale=0.8, 
                   instance_mode=ColorMode.IMAGE_BW   # remove the colors of unsegmented pixels
    )
    v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    cv2_imshow(v.get_image()[:, :, ::-1])